Event discrimination algorithms are widely used to detect the occurrence of a specified condition or event that is not susceptible to direct measurement. For example, a restraint deployment algorithm in a motor vehicle is designed to detect the occurrence of a crash event severe enough to warrant deployment of passenger restraints based on measured acceleration data and the like, while discriminating against events that are not sufficiently severe to warrant deployment of the passenger restraints. Other automotive examples include algorithms for detecting an impending rollover event, and algorithms for discriminating between frontal impacts and side impacts.
In general, event discrimination algorithms are developed by analyzing various sets of data measured during both events and non-events, and recognizing data patterns that can be used to discriminate between events and non-events. For example, the U.S. Pat. No. 6,542,792 to Schubert et al. discloses a rollover detection algorithm in which an operating point of the vehicle defined by its roll rate and roll angle is compared with a calibrated threshold that divides the roll rate vs. roll angle space into rollover events and non-rollover events. While calibrating an algorithm to reliably discriminate a specified event for any one data set is not difficult, calibrating the algorithm to reliably discriminate a specified event for multiple data sets can be very difficult. For example, suppose input data sets for three deploy events A, B, C and three non-deploy events 1, 2, 3 are available. In principle, the event discrimination algorithm must be calibrated to distinguish events A-C vs. events 1-3. If a new input data set for deploy event D becomes available and the algorithm fails to correctly identify D as a deploy event, the algorithm must be re-calibrated to distinguish events A-D vs. events 1-3. It will be appreciated that this can be an extremely difficult and time consuming procedure, possibly requiring re-evaluation of the algorithm framework in addition to re-calibrating various algorithm thresholds. Accordingly, what is needed is a more effective way of adapting an event discrimination algorithm to both current and future input data sets without sacrificing discrimination reliability or delaying detection of the specified event.